Bibliographic record
Abstract
Our tour begins with the transformation of poque — a French and Persian parlor diversion — into a bawdy frontier game favoring the most effective cheaters. It then follows through to the gentrification of poker and, finally, its meteoric rise in popularity after the introduction of the “pocket cam,” which evolved poker from a table game into a profitable spectator sport. McManus examines in great detail how this metamorphosis represents the changing face of American culture, from fledgling frontier nation to dominant political and economic force, from outlaws in dirty saloons to average Joes and Janes in spotless, well-lit poker rooms complete with hand sanitizer dispensers. One of the more interesting, and justifiably controversial, concepts is that of a supposed “immigrant gene” and its potential influence on American history and culture. McManus cites Peter C. Whybrow (2005), a behavioral scientist at UCLA, who asks (as if wondering aloud) why Canadians, South Americans, and other Western colonial cultures do not seem as predisposed to move inexorably from one risk to the next, in search of greater and greater reward, as US culture (unfortunately, he provides no evidence for this observation). Relying heavily on Whybrow’s suggestion of a genetic marker that predisposes certain individuals to search for greener pastures during difficult times, McManus indicates that the US’s unique history may have facilitated the concentration of this as-yet unidentified gene in the U.S. population. Much of the book is concerned with cheating, bluffing, and reading opponents’ bluffs — skills that can prove invaluable at the table, as well as in other high-stakes situations. In many cases, effective leadership involves winning decisive victories from positions of weakness. One of the many examples he related was a story about Nathan Bedford Forrest, a confederate general in the Civil War, infamous for his merciless treatment of enemy combatants and civilians (and even more infamous for starting the Ku Klux Klan). Forrest pulled off a monumental bluff, convincing Colonel Abel Streight to surrender, even though Streight had Forrest significantly outnumbered and outgunned. (The humiliated Colonel
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".